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Activity Number:
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38
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Type:
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Invited
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Date/Time:
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Sunday, July 29, 2007 : 4:00 PM to 5:50 PM
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Sponsor:
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Business and Economics Statistics Section
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| Abstract - #307668 |
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Title:
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Interday Forecasting and Intraday Updating of Call Center Arrivals
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Author(s):
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Haipeng Shen*+ and Jianhua Z. Huang
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Companies:
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The University of North Carolina at Chapel Hill and Texas A&M University
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Address:
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304 Smith Building, Chapel Hill, NC, 27599,
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Keywords:
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dimension reduction ; dynamic forecast updating ; principal component analysis ; penalized least squares ; singular value decomposition ; vector time series
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Abstract:
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Accurate forecasting of call arrivals is critical for staffing and scheduling of a call center. We develop methods for interday and intraday forecasting of incoming call volumes. Our approach is to treat the intraday call volume profiles as a high-dimensional vector time series. We propose to first reduce the dimensionality by singular value decomposition of the matrix of historical intraday profiles and then apply time series and regression techniques. Both interday dynamics and intraday patterns of call arrivals are taken into account by our approach. Distributional forecasts are also developed. Our methods are data-driven, and appear to be robust against model assumptions in simulation studies. They are shown to be very competitive against existing approaches in out-of-sample forecast exercises using real data. Our methods are computationally fast for real time dynamic forecasting.
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